Digital photography is a type of photography that uses digital technology to make digital images of subjects. Digital image capturing devices, such as digital cameras, include sensors that capture images in a digital form. Examples of such sensors include charge coupled devices (CCDs) and CMOS (complementary metal-oxide-semiconductor) sensors. Digital images can be displayed, printed, stored, manipulated, transmitted, and archived without chemical processing.
A digital image capturing device typically includes a two-dimensional array of sensor elements organized into rows and columns. Such a sensor array may have any number of pixel sensors, including thousands or millions of pixel sensors. Each pixel sensor of the sensor array may be configured to be sensitive to a specific color, or color range, such as through the use of color filters. In one example, three types of pixel sensors may be present, including a first set of pixel sensors that are sensitive to the color red, a second set of pixel sensors that are sensitive to green, and a third set of pixel sensors that are sensitive to blue. One common example distribution of pixel sensors that are sensitive to red, green, or blue is called a Bayer pattern. In a Bayer pattern, 50% of the pixel sensors are configured to be sensitive to green, 25% of the pixel sensors are configured to be sensitive to red, and 25% of the pixel sensors are configured to be sensitive to blue. A set of red, green, and blue pixels captured according to a Bayer pattern may be processed and stored as a RGB (red-green-blue) color model image.
Many types of color models exist, including RGB, YUV, HSV, YIQ, and Lab color models. Many of such color models define an image in terms of a luminance channel and one or more chrominance channels, rather than in terms of channels that mix both luminance and chrominance together (as in RGB). For instance, according to the YUV and YIQ models, a color space is defined in terms of one luminance (Y) and two chrominance (UV) channels. The HSV model describes a color space as points in a cylinder, according to hue (chrominance), saturation (chrominance), and brightness (luminance). According to the Lab color model, a color space is defined in terms of one lightness component (L) and two color components (ab). Images according to these color models are typically digitally converted from a captured RGB image (or other chrominance-specific image model).
A problem in digital photography is that the color data, as seen in the chrominance channel(s) of an image, is often inaccurate. This problem is especially predominant with regard to images that were poorly illuminated when captured. As a result, the color data of such an image may be noisy. This inaccuracy in color data can result in undesirable color artifacts being present in images, particularly when the images are viewed at high levels of magnification. Relatively simple approaches for correcting this problem (e.g., by blurring the chrominance) can cause an image to be overly smoothed. More advanced edge-preserving approaches for correcting the problem can be applied, but may result in splotchy colors to be present in the output image that are even worse than the pre-corrected colors. This is because the edge-preserving filters must detect edges in the chrominance data to operate correctly, and their assumptions may be insufficiently robust when the color data is inaccurate. Thus, ways of improving the accuracy of color data in digital images are desired that do not blur and/or otherwise substantially reduce image quality.